anotherjesse / dogbooth
- Public
- 100 runs
-
A100 (80GB)
Prediction
anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666eaIDi7gqskz5zvbjnfhzv6zw2qg2hmStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 9693
- width
- "512"
- height
- "512"
- prompt
- dgg as a pixar character
- scheduler
- DDIM
- num_outputs
- "1"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 9693, "width": "512", "height": "512", "prompt": "dgg as a pixar character", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", { input: { seed: 9693, width: "512", height: "512", prompt: "dgg as a pixar character", scheduler: "DDIM", num_outputs: "1", guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", input={ "seed": 9693, "width": "512", "height": "512", "prompt": "dgg as a pixar character", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", "input": { "seed": 9693, "width": "512", "height": "512", "prompt": "dgg as a pixar character", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-13T03:26:51.428033Z", "created_at": "2022-12-13T03:26:48.136110Z", "data_removed": false, "error": null, "id": "i7gqskz5zvbjnfhzv6zw2qg2hm", "input": { "seed": 9693, "width": "512", "height": "512", "prompt": "dgg as a pixar character", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 9693\nusing weights: True\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:02, 17.03it/s]\n 8%|▊ | 4/50 [00:00<00:02, 17.76it/s]\n 12%|█▏ | 6/50 [00:00<00:02, 17.90it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 18.06it/s]\n 20%|██ | 10/50 [00:00<00:02, 18.20it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 18.31it/s]\n 28%|██▊ | 14/50 [00:00<00:01, 18.39it/s]\n 32%|███▏ | 16/50 [00:00<00:01, 18.49it/s]\n 36%|███▌ | 18/50 [00:00<00:01, 18.59it/s]\n 40%|████ | 20/50 [00:01<00:01, 18.55it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 18.63it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 18.53it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 18.39it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 18.40it/s]\n 60%|██████ | 30/50 [00:01<00:01, 18.14it/s]\n 64%|██████▍ | 32/50 [00:01<00:00, 18.27it/s]\n 68%|██████▊ | 34/50 [00:01<00:00, 18.38it/s]\n 72%|███████▏ | 36/50 [00:01<00:00, 18.44it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 18.48it/s]\n 80%|████████ | 40/50 [00:02<00:00, 18.41it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 18.48it/s]\n 88%|████████▊ | 44/50 [00:02<00:00, 18.49it/s]\n 92%|█████████▏| 46/50 [00:02<00:00, 18.53it/s]\n 96%|█████████▌| 48/50 [00:02<00:00, 18.55it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.44it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.37it/s]", "metrics": { "predict_time": 3.25066, "total_time": 3.291923 }, "output": [ "https://replicate.delivery/pbxt/2kuSK2PJU46ZINJcoAeueNHEtIFOxnycKViMfVqfLdktvolAB/out-0.png" ], "started_at": "2022-12-13T03:26:48.177373Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/i7gqskz5zvbjnfhzv6zw2qg2hm", "cancel": "https://api.replicate.com/v1/predictions/i7gqskz5zvbjnfhzv6zw2qg2hm/cancel" }, "version": "61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea" }Generated inUsing seed: 9693 using weights: True 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 17.03it/s] 8%|▊ | 4/50 [00:00<00:02, 17.76it/s] 12%|█▏ | 6/50 [00:00<00:02, 17.90it/s] 16%|█▌ | 8/50 [00:00<00:02, 18.06it/s] 20%|██ | 10/50 [00:00<00:02, 18.20it/s] 24%|██▍ | 12/50 [00:00<00:02, 18.31it/s] 28%|██▊ | 14/50 [00:00<00:01, 18.39it/s] 32%|███▏ | 16/50 [00:00<00:01, 18.49it/s] 36%|███▌ | 18/50 [00:00<00:01, 18.59it/s] 40%|████ | 20/50 [00:01<00:01, 18.55it/s] 44%|████▍ | 22/50 [00:01<00:01, 18.63it/s] 48%|████▊ | 24/50 [00:01<00:01, 18.53it/s] 52%|█████▏ | 26/50 [00:01<00:01, 18.39it/s] 56%|█████▌ | 28/50 [00:01<00:01, 18.40it/s] 60%|██████ | 30/50 [00:01<00:01, 18.14it/s] 64%|██████▍ | 32/50 [00:01<00:00, 18.27it/s] 68%|██████▊ | 34/50 [00:01<00:00, 18.38it/s] 72%|███████▏ | 36/50 [00:01<00:00, 18.44it/s] 76%|███████▌ | 38/50 [00:02<00:00, 18.48it/s] 80%|████████ | 40/50 [00:02<00:00, 18.41it/s] 84%|████████▍ | 42/50 [00:02<00:00, 18.48it/s] 88%|████████▊ | 44/50 [00:02<00:00, 18.49it/s] 92%|█████████▏| 46/50 [00:02<00:00, 18.53it/s] 96%|█████████▌| 48/50 [00:02<00:00, 18.55it/s] 100%|██████████| 50/50 [00:02<00:00, 18.44it/s] 100%|██████████| 50/50 [00:02<00:00, 18.37it/s]Prediction
anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666eaIDnbm73tluafe37cqdpgwlx2zzeyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 19718
- width
- "512"
- height
- "512"
- prompt
- a photo of dgg wearing sunglasses
- scheduler
- DDIM
- num_outputs
- "1"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 19718, "width": "512", "height": "512", "prompt": "a photo of dgg wearing sunglasses", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", { input: { seed: 19718, width: "512", height: "512", prompt: "a photo of dgg wearing sunglasses", scheduler: "DDIM", num_outputs: "1", guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", input={ "seed": 19718, "width": "512", "height": "512", "prompt": "a photo of dgg wearing sunglasses", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", "input": { "seed": 19718, "width": "512", "height": "512", "prompt": "a photo of dgg wearing sunglasses", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-13T03:27:41.827851Z", "created_at": "2022-12-13T03:27:38.573553Z", "data_removed": false, "error": null, "id": "nbm73tluafe37cqdpgwlx2zzey", "input": { "seed": 19718, "width": "512", "height": "512", "prompt": "a photo of dgg wearing sunglasses", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 19718\nusing weights: True\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:02, 17.43it/s]\n 8%|▊ | 4/50 [00:00<00:02, 17.81it/s]\n 12%|█▏ | 6/50 [00:00<00:02, 18.11it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 18.21it/s]\n 20%|██ | 10/50 [00:00<00:02, 18.25it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 18.25it/s]\n 28%|██▊ | 14/50 [00:00<00:01, 18.32it/s]\n 32%|███▏ | 16/50 [00:00<00:01, 18.34it/s]\n 36%|███▌ | 18/50 [00:00<00:01, 18.14it/s]\n 40%|████ | 20/50 [00:01<00:01, 18.21it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 18.29it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 18.31it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 18.36it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 18.42it/s]\n 60%|██████ | 30/50 [00:01<00:01, 18.47it/s]\n 64%|██████▍ | 32/50 [00:01<00:00, 18.56it/s]\n 68%|██████▊ | 34/50 [00:01<00:00, 18.60it/s]\n 72%|███████▏ | 36/50 [00:01<00:00, 18.36it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 18.46it/s]\n 80%|████████ | 40/50 [00:02<00:00, 18.55it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 18.52it/s]\n 88%|████████▊ | 44/50 [00:02<00:00, 18.49it/s]\n 92%|█████████▏| 46/50 [00:02<00:00, 18.55it/s]\n 96%|█████████▌| 48/50 [00:02<00:00, 18.59it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.53it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.38it/s]", "metrics": { "predict_time": 3.215414, "total_time": 3.254298 }, "output": [ "https://replicate.delivery/pbxt/YvxOYBDXoyYnH1e1M3JsVxxh7nqEnwrAGvguzpalpO9WGtEIA/out-0.png" ], "started_at": "2022-12-13T03:27:38.612437Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nbm73tluafe37cqdpgwlx2zzey", "cancel": "https://api.replicate.com/v1/predictions/nbm73tluafe37cqdpgwlx2zzey/cancel" }, "version": "61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea" }Generated inUsing seed: 19718 using weights: True 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 17.43it/s] 8%|▊ | 4/50 [00:00<00:02, 17.81it/s] 12%|█▏ | 6/50 [00:00<00:02, 18.11it/s] 16%|█▌ | 8/50 [00:00<00:02, 18.21it/s] 20%|██ | 10/50 [00:00<00:02, 18.25it/s] 24%|██▍ | 12/50 [00:00<00:02, 18.25it/s] 28%|██▊ | 14/50 [00:00<00:01, 18.32it/s] 32%|███▏ | 16/50 [00:00<00:01, 18.34it/s] 36%|███▌ | 18/50 [00:00<00:01, 18.14it/s] 40%|████ | 20/50 [00:01<00:01, 18.21it/s] 44%|████▍ | 22/50 [00:01<00:01, 18.29it/s] 48%|████▊ | 24/50 [00:01<00:01, 18.31it/s] 52%|█████▏ | 26/50 [00:01<00:01, 18.36it/s] 56%|█████▌ | 28/50 [00:01<00:01, 18.42it/s] 60%|██████ | 30/50 [00:01<00:01, 18.47it/s] 64%|██████▍ | 32/50 [00:01<00:00, 18.56it/s] 68%|██████▊ | 34/50 [00:01<00:00, 18.60it/s] 72%|███████▏ | 36/50 [00:01<00:00, 18.36it/s] 76%|███████▌ | 38/50 [00:02<00:00, 18.46it/s] 80%|████████ | 40/50 [00:02<00:00, 18.55it/s] 84%|████████▍ | 42/50 [00:02<00:00, 18.52it/s] 88%|████████▊ | 44/50 [00:02<00:00, 18.49it/s] 92%|█████████▏| 46/50 [00:02<00:00, 18.55it/s] 96%|█████████▌| 48/50 [00:02<00:00, 18.59it/s] 100%|██████████| 50/50 [00:02<00:00, 18.53it/s] 100%|██████████| 50/50 [00:02<00:00, 18.38it/s]Prediction
anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666eaIDumfdfqshkzaxxl3eaedgnmjyeaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 64596
- width
- "512"
- height
- "512"
- prompt
- a velvet painting of dgg playing poker
- scheduler
- DDIM
- num_outputs
- "1"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 64596, "width": "512", "height": "512", "prompt": "a velvet painting of dgg playing poker", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", { input: { seed: 64596, width: "512", height: "512", prompt: "a velvet painting of dgg playing poker", scheduler: "DDIM", num_outputs: "1", guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", input={ "seed": 64596, "width": "512", "height": "512", "prompt": "a velvet painting of dgg playing poker", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "anotherjesse/dogbooth:61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea", "input": { "seed": 64596, "width": "512", "height": "512", "prompt": "a velvet painting of dgg playing poker", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-13T03:27:47.631709Z", "created_at": "2022-12-13T03:27:44.383106Z", "data_removed": false, "error": null, "id": "umfdfqshkzaxxl3eaedgnmjyea", "input": { "seed": 64596, "width": "512", "height": "512", "prompt": "a velvet painting of dgg playing poker", "scheduler": "DDIM", "num_outputs": "1", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 64596\nusing weights: True\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:02, 17.81it/s]\n 8%|▊ | 4/50 [00:00<00:02, 17.40it/s]\n 12%|█▏ | 6/50 [00:00<00:02, 17.85it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 17.88it/s]\n 20%|██ | 10/50 [00:00<00:02, 17.75it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 17.88it/s]\n 28%|██▊ | 14/50 [00:00<00:01, 18.10it/s]\n 32%|███▏ | 16/50 [00:00<00:01, 18.25it/s]\n 36%|███▌ | 18/50 [00:00<00:01, 18.35it/s]\n 40%|████ | 20/50 [00:01<00:01, 18.40it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 18.38it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 18.31it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 18.31it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 18.32it/s]\n 60%|██████ | 30/50 [00:01<00:01, 18.34it/s]\n 64%|██████▍ | 32/50 [00:01<00:00, 18.48it/s]\n 68%|██████▊ | 34/50 [00:01<00:00, 18.55it/s]\n 72%|███████▏ | 36/50 [00:01<00:00, 18.52it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 18.58it/s]\n 80%|████████ | 40/50 [00:02<00:00, 18.34it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 18.46it/s]\n 88%|████████▊ | 44/50 [00:02<00:00, 18.43it/s]\n 92%|█████████▏| 46/50 [00:02<00:00, 18.45it/s]\n 96%|█████████▌| 48/50 [00:02<00:00, 18.37it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.38it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.28it/s]", "metrics": { "predict_time": 3.209735, "total_time": 3.248603 }, "output": [ "https://replicate.delivery/pbxt/lYHXrMvQiO5pKpW4fl82oeHyScJk79EnmKf4SmpmOjCmZ0SgA/out-0.png" ], "started_at": "2022-12-13T03:27:44.421974Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/umfdfqshkzaxxl3eaedgnmjyea", "cancel": "https://api.replicate.com/v1/predictions/umfdfqshkzaxxl3eaedgnmjyea/cancel" }, "version": "61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea" }Generated inUsing seed: 64596 using weights: True 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 17.81it/s] 8%|▊ | 4/50 [00:00<00:02, 17.40it/s] 12%|█▏ | 6/50 [00:00<00:02, 17.85it/s] 16%|█▌ | 8/50 [00:00<00:02, 17.88it/s] 20%|██ | 10/50 [00:00<00:02, 17.75it/s] 24%|██▍ | 12/50 [00:00<00:02, 17.88it/s] 28%|██▊ | 14/50 [00:00<00:01, 18.10it/s] 32%|███▏ | 16/50 [00:00<00:01, 18.25it/s] 36%|███▌ | 18/50 [00:00<00:01, 18.35it/s] 40%|████ | 20/50 [00:01<00:01, 18.40it/s] 44%|████▍ | 22/50 [00:01<00:01, 18.38it/s] 48%|████▊ | 24/50 [00:01<00:01, 18.31it/s] 52%|█████▏ | 26/50 [00:01<00:01, 18.31it/s] 56%|█████▌ | 28/50 [00:01<00:01, 18.32it/s] 60%|██████ | 30/50 [00:01<00:01, 18.34it/s] 64%|██████▍ | 32/50 [00:01<00:00, 18.48it/s] 68%|██████▊ | 34/50 [00:01<00:00, 18.55it/s] 72%|███████▏ | 36/50 [00:01<00:00, 18.52it/s] 76%|███████▌ | 38/50 [00:02<00:00, 18.58it/s] 80%|████████ | 40/50 [00:02<00:00, 18.34it/s] 84%|████████▍ | 42/50 [00:02<00:00, 18.46it/s] 88%|████████▊ | 44/50 [00:02<00:00, 18.43it/s] 92%|█████████▏| 46/50 [00:02<00:00, 18.45it/s] 96%|█████████▌| 48/50 [00:02<00:00, 18.37it/s] 100%|██████████| 50/50 [00:02<00:00, 18.38it/s] 100%|██████████| 50/50 [00:02<00:00, 18.28it/s]
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